Danyu Lin (Biostatistician)
Danyu Lin (
Chinese : 林丹瑜 ) is a Chinese-American
biostatistician known for his contributions to
survival analysis ,
statistical genetics , and
infectious diseases . He is currently the
Dennis Gillings Distinguished Professor
[1] of
Biostatistics at the
University of North Carolina at Chapel Hill .
Research
Lin's early work in
survival analysis focused on marginal models for multivariate failure time data, robust inference, and model checking.
[2]
[3]
[4]
[5]
[6] The statistical methods he developed have been incorporated into major textbooks
[7]
[8] and software packages (
SAS ,
R ,
Stata , SUDDAN
[9] ) and used in thousands of scientific studies.
[10] Lin also conducted groundbreaking research in semiparametric additive risks models and accelerated failure time models.
[11]
[12] Over the last two decades, Lin has made major theoretical and computational advances in nonparametric maximum likelihood estimation of transformation models, random-effects models, and interval-censored data.
[13]
[14]
Lin has made seminal contributions to
statistical genetics . His finding that meta-analysis of summary statistics is equivalent to joint analysis of individual-participant data
[15]
[16] has enabled geneticists around the world to discover hundreds of thousands of genetic variants associated with thousands of complex human diseases and traits through meta-analyses of genome-wide association studies and next-generation sequencing studies. He also pioneered the use of score statistics in genetic association studies,
[17]
[18] which substantially speeds up computation for genome-wide association tests.
Lin made important contributions to the prevention and treatment of COVID-19 by characterizing the time-varying effects of vaccines and prior infections, as well as the benefits of antiviral drugs. His high-profile publications (5 in
New England Journal of Medicine , 3 in
JAMA journals, and 2 in
The Lancet journals)
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28] have been viewed over 1 million times; cited by the
U.S. Food and Drug Administration ,
[29]
[30]
Centers for Disease Control and Prevention ,
[31] and the
World Health Organization ;
[32] and reported by
The New York Times ,
[33]
[34]
The Washington Post ,
[35]
[36]
[37]
[38]
The US News ,
[39]
The Associated Press ,
[40]
The Wall Street Journal ,
[41]
NBC News ,
[42]
Science ,
[43]
[44] and
Scientific American .
[45]
Career
Lin received his
Ph.D. in
Biostatistics in 1989 from the
University of Michigan , where he was supervised by
Lee-Jen Wei . After one-year post-doctoral training with Stephen Lagakos at
Harvard University , he joined the
Biostatistics faculty at the
University of Washington , where he was promoted to Associate Professor in 2004 and to Professor in 2008. He also held a joint appointment with the
Fred Hutchinson Cancer Research Center . Lin moved to the
University of North Carolina at Chapel Hill at the end of 2020 to become the
Dennis Gillings Distinguished Professor of
Biostatistics .
Lin served as an Associate Editor for numerous statistical journals, including
Biometrics (1997-2000),
Biometrika (1999-2023),
Journal of the American Statistical Association (2012-2023). He also served as a Special Government Employee (Consultant) to the
U.S. Food and Drug Administration . He currently serves on the Editorial Board of
Genetic Epidemiology and as a Statistical Reviewer for
The Lancet Infectious Diseases .
Honors and Awards
References
^
"Danyu Lin, PhD" .
UNC Gillings School of Global Public Health . Retrieved May 7, 2024 .
^ Wei LJ, Lin DY, Weissfeld L (1989).
Regression analysis of multivariate incomplete failure time data by modeling marginal distributions .
Journal of the American Statistical Association 84: 1065-1073.
^ Lin DY, Wei LJ (1989).
The robust inference for the Cox proportional hazards model .
Journal of the American Statistical Association 84: 1074-1078.
^ Lin DY, Wei LJ, Ying Z (1993).
Checking the Cox model with cumulative sums of martingale-based residuals .
Biometrika 80: 557-572.
^ Lin DY (1994).
Cox regression analysis of multivariate failure time data: the marginal approach .
Statistics in Medicine 13: 2233-2247.
^ Lin DY, Wei LJ, Yang I, Ying Z (2000).
Semiparametric regression for the mean and rate functions of recurrent events .
Journal of the Royal Statistical Society - Series B 62: 711-730.
^ Kalbfleisch JD, Prentice RL (2002).
The Statistical Analysis of Failure Time Data .
John Wiley & Sons .
^ Klein JP, Moeschberger ML (2003).
Survival Analysis: Techniques for Censored and Truncated Data . New York:
Springer .
^
"SUDDAN: Statistical Software for Weighting, Imputing, and Analyzing Data" . Retrieved May 7, 2024 .
^ Google Scholar
[1]
^ Lin DY, Ying Z (1994).
Semiparametric analysis of the additive risk model .
Biometrika 81: 61-71.
^ Jin Z, Lin DY, Wei LJ, Ying Z (2023).
Rank‐based inference for the accelerated failure time model .
Biometrika 90: 341-353.
^ Zeng D, Lin DY (2007).
Maximum likelihood estimation in semiparametric regression models with censored data (with discussion) .
Journal of the Royal Statistical Society - Series B 69: 507-564.
^ Zeng D, Mao L, Lin DY (2016).
Maximum likelihood estimation for semiparametric transformation models with interval-censored data .
Biometrika 103: 253-271.
^ Lin DY, Zeng D (2010).
Meta-analysis of genome-wide association studies: No efficiency gain in using individual participant data.
Genetic Epidemiology 34: 60-66
^ Lin DY, Zeng D (2010).
On the relative efficiency of using summary statistics versus individual-level data in meta-analysis .
Biometrika 97: 321-332.
^ Lin DY (2006).
Evaluating statistical significance in two-stage genomewide association studies .
American Journal of Human Genetics 78: 505-509.
^ Lin, DY, Tang ZZ (2011).
A general framework for detecting disease associations with rare variants in sequencing studies .
American Journal of Human Genetics 89: 354-367.
^ Lin DY, Baden LR, El Sahly HM, Issink B, Neuzil KM, Corey L, Miller J for the COVE Study Group (2022).
Durability of Protection Against Symptomatic COVID-19 Among Participants of the mRNA-1273 SARS-CoV-2 Vaccine Trial .
JAMA Network Open 5: e2215984
^ Lin DY, Gu Y, Wheeler B, Young H, Holloway S, Sunny SK, Moore Z, Zeng D (2022).
Effectiveness of COVID-19 vaccines over a 9-month period in North Carolina .
New England Journal of Medicine 386: 933-941.
^ Lin DY, Gu Y, Xu Y, Zeng D, Wheeler B, Young H, Sunny SK, Moore Z (2022).
Effects of vaccination and previous infection on omicron infections in children .
New England Journal of Medicine 387: 1141-1143.
^ Lin DY, Gu Y, Xu Y, Wheeler B, Young H, Sunny SK, Moore Z, Zeng D (2022).
Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes .
JAMA 338: 1415-1426.
^ Lin DY, Xu Y, Zeng D, Wheeler B, Young H, Moore Z, Sunny SK (2023).
Effects of COVID-19 vaccination and previous SARS-CoV-2 infection on omicron infection and severe outcomes in children under 12 years of age in the USA: an observational cohort study .
The Lancet Infectious Diseases 23: 1257-1265.
^ Lin DY, Xu Y, Gu Y, Zeng D, Wheeler B, Young H, Sunny SK, Moore Z (2023).
Effectiveness of Bivalent Boosters against Severe Omicron Infection .
New England Journal of Medicine 388: 764-766.
^ Lin DY, Xu Y, Gu Y, Zeng D, Sunny SK, Moore Z (2023).
Durability of Bivalent Boosters against Omicron Subvariants .
New England Journal of Medicine 388: 1818-1820
^ Lin DY, Abi Fadel F, Huang S, Milinovich AT, Sacha GL, Bartley P, Duggal A, Wang X (2023).
Nirmatrelvir or Molnupiravir Use and Severe Outcomes From Omicron Infections .
JAMA Network Open 6: e2335077.
^ Lin DY, Huang S, Milinovich A, Duggal A, Wang X (2024).
Effectiveness of XBB.1.5 vaccines and antiviral drugs against severe outcomes of omicron infection in the USA .
The Lancet Infectious Diseases 24: 278-280.
^ Lin DY, Du Y, Xu Y, Paritala S, Donahue, M, and Maloney P (2024).
Durability of XBB.1.5 Vaccines against Omicron Subvariants .
New England Journal of Medicine .
^ Weir, Jerry (January 26, 2023).
"Consideration for Potential Changes to COVID-19 Vaccine Strain Composition" .
FDA .
^ Weir, Jerry (June 5, 2024).
"FDA Considerations and Recommendations for the 2024-2025 COVID-19 Vaccine Formula Composition" .
FDA .
^
Centers for Disease Control and Prevention (January 13, 2022).
"COVID-19 weekly update : Up to date genomics and precision health information on COVID-19" .
^
World Health Organization (October 26, 2022).
"COVID-19 weekly epidemiological update, edition 115, 26 October 2022" .
^ Mueller, Benjamin; Lafraniere, Sharon (January 26, 2023).
"Covid Vaccines Targeting Omicron Should be Standard, Panel Says" .
The New York Times . {{
cite web }}
: CS1 maint: multiple names: authors list (
link )
^ Smith, Dana G. (February 2, 2023).
"Who Should Get a Covid Booster Now? New Data Offers Some Clarity" .
The New York Times .
^ Krause, Phillip; Gruber, Marion; Offit, Paul (November 29, 2021).
"We don't need universal booster shots. We need to reach the unvaccinated" .
The Washington Post . {{
cite news }}
: CS1 maint: multiple names: authors list (
link )
^ Wen, Leana (October 20, 2022).
"Opinion | The Checkup With Dr. Wen: Should all children get the updated booster?" .
The Washington Post .
^ Wen, Leana (February 7, 2023).
"Opinion | Should there be an annual coronavirus booster? It depends" .
The Washington Post .
^ Wen, Leana (October 5, 2023).
"Opinion | The Checkup With Dr. Wen: Paxlovid might be even more important than the new covid shot" .
The Washington Post .
^ Foster, Robin (January 27, 2023).
"Updated Booster Shots, Not Original COVID Vaccines, Should Be Standard: FDA Panel" .
US News .
^ Kelety, Josh (September 15, 2022).
"Study finds Pfizer vaccine boosts, not destroys, immunity from past COVID-19 infection" .
Associated Press News .
^ Finley, Allysia (January 29, 2023).
"Opinion | How Biden Officials Bungled a Better Vaccine" .
WSJ .
^ Ryan, Benjamin (September 24, 2023).
"As Covid cases rise, what to know about Paxlovid" .
NBC News .
^ Lowe, Derek (February 16, 2023).
"There Are Vaccines and There Are Vaccines" .
Science .
^ Couzin-Frankel, Jennifer (May 23, 2023).
"COVID-19 vaccines may undergo major overhaul this fall" .
Science .
^ Young, Lauren (June 5, 2024).
"New 'FLiRT' COVID Variants Could Be Driving an Uptick in Cases. Here's How to Avoid Them" .
Scientific American .
^
"Awards" . Retrieved May 7, 2024 .
^
"Scientific Legacy Database" . Institute of Mathematical Studies . Retrieved May 7, 2024 .
^
"ASA Fellows" .
American Statistical Association . Retrieved May 7, 2024 .
^
"2015 G. W. Snedecor Award Winner" .
Committee of Presidents of Statistical Societies . Retrieved May 7, 2024 .